/usr/lib/python2.7/dist-packages/tables/vlarray.py is in python-tables 3.1.1-0ubuntu1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 | # -*- coding: utf-8 -*-
########################################################################
#
# License: BSD
# Created: November 12, 2003
# Author: Francesc Alted - faltet@pytables.com
#
# $Id$
#
########################################################################
"""Here is defined the VLArray class."""
import sys
import numpy
from tables import hdf5extension
from tables.utils import (convert_to_np_atom, convert_to_np_atom2, idx2long,
correct_byteorder, SizeType, is_idx, lazyattr)
from tables.atom import ObjectAtom, VLStringAtom, VLUnicodeAtom
from tables.flavor import internal_to_flavor
from tables.leaf import Leaf, calc_chunksize
from tables._past import previous_api, previous_api_property
# default version for VLARRAY objects
# obversion = "1.0" # initial version
# obversion = "1.0" # add support for complex datatypes
# obversion = "1.1" # This adds support for time datatypes.
# obversion = "1.2" # This adds support for enumerated datatypes.
# obversion = "1.3" # Introduced 'PSEUDOATOM' attribute.
obversion = "1.4" # Numeric and numarray flavors are gone.
class VLArray(hdf5extension.VLArray, Leaf):
"""This class represents variable length (ragged) arrays in an HDF5 file.
Instances of this class represent array objects in the object tree
with the property that their rows can have a *variable* number of
homogeneous elements, called *atoms*. Like Table datasets (see
:ref:`TableClassDescr`), variable length arrays can have only one
dimension, and the elements (atoms) of their rows can be fully
multidimensional.
When reading a range of rows from a VLArray, you will *always* get
a Python list of objects of the current flavor (each of them for a
row), which may have different lengths.
This class provides methods to write or read data to or from
variable length array objects in the file. Note that it also
inherits all the public attributes and methods that Leaf (see
:ref:`LeafClassDescr`) already provides.
.. note::
VLArray objects also support compression although compression
is only performed on the data structures used internally by
the HDF5 to take references of the location of the variable
length data. Data itself (the raw data) are not compressed
or filtered.
Please refer to the `VLTypes Technical Note
<http://www.hdfgroup.org/HDF5/doc/TechNotes/VLTypes.html>`_
for more details on the topic.
Parameters
----------
parentnode
The parent :class:`Group` object.
.. versionchanged:: 3.0
Renamed from *parentNode* to *parentnode*.
name : str
The name of this node in its parent group.
atom
An `Atom` instance representing the *type* and *shape* of the atomic
objects to be saved.
title
A description for this node (it sets the ``TITLE`` HDF5 attribute on
disk).
filters
An instance of the `Filters` class that provides information about the
desired I/O filters to be applied during the life of this object.
expectedrows
A user estimate about the number of row elements that will
be added to the growable dimension in the `VLArray` node.
If not provided, the default value is ``EXPECTED_ROWS_VLARRAY``
(see ``tables/parameters.py``). If you plan to create either
a much smaller or a much bigger `VLArray` try providing a guess;
this will optimize the HDF5 B-Tree creation and management
process time and the amount of memory used.
.. versionadded:: 3.0
chunkshape
The shape of the data chunk to be read or written in a single HDF5 I/O
operation. Filters are applied to those chunks of data. The
dimensionality of `chunkshape` must be 1. If ``None``, a sensible
value is calculated (which is recommended).
byteorder
The byteorder of the data *on disk*, specified as 'little' or 'big'.
If this is not specified, the byteorder is that of the platform.
.. versionchanged:: 3.0
The *expectedsizeinMB* parameter has been replaced by *expectedrows*.
Examples
--------
See below a small example of the use of the VLArray class. The code is
available in :file:`examples/vlarray1.py`::
import tables
from numpy import *
# Create a VLArray:
fileh = tables.open_file('vlarray1.h5', mode='w')
vlarray = fileh.create_vlarray(fileh.root, 'vlarray1',
tables.Int32Atom(shape=()),
"ragged array of ints",
filters=tables.Filters(1))
# Append some (variable length) rows:
vlarray.append(array([5, 6]))
vlarray.append(array([5, 6, 7]))
vlarray.append([5, 6, 9, 8])
# Now, read it through an iterator:
print('-->', vlarray.title)
for x in vlarray:
print('%s[%d]--> %s' % (vlarray.name, vlarray.nrow, x))
# Now, do the same with native Python strings.
vlarray2 = fileh.create_vlarray(fileh.root, 'vlarray2',
tables.StringAtom(itemsize=2),
"ragged array of strings",
filters=tables.Filters(1))
vlarray2.flavor = 'python'
# Append some (variable length) rows:
print('-->', vlarray2.title)
vlarray2.append(['5', '66'])
vlarray2.append(['5', '6', '77'])
vlarray2.append(['5', '6', '9', '88'])
# Now, read it through an iterator:
for x in vlarray2:
print('%s[%d]--> %s' % (vlarray2.name, vlarray2.nrow, x))
# Close the file.
fileh.close()
The output for the previous script is something like::
--> ragged array of ints
vlarray1[0]--> [5 6]
vlarray1[1]--> [5 6 7]
vlarray1[2]--> [5 6 9 8]
--> ragged array of strings
vlarray2[0]--> ['5', '66']
vlarray2[1]--> ['5', '6', '77']
vlarray2[2]--> ['5', '6', '9', '88']
.. rubric:: VLArray attributes
The instance variables below are provided in addition to those in
Leaf (see :ref:`LeafClassDescr`).
.. attribute:: atom
An Atom (see :ref:`AtomClassDescr`)
instance representing the *type* and
*shape* of the atomic objects to be
saved. You may use a *pseudo-atom* for
storing a serialized object or variable length string per row.
.. attribute:: flavor
The type of data object read from this leaf.
Please note that when reading several rows of VLArray data,
the flavor only applies to the *components* of the returned
Python list, not to the list itself.
.. attribute:: nrow
On iterators, this is the index of the current row.
.. attribute:: nrows
The current number of rows in the array.
.. attribute:: extdim
The index of the enlargeable dimension (always 0 for vlarrays).
"""
# Class identifier.
_c_classid = 'VLARRAY'
_c_classId = previous_api_property('_c_classid')
# Lazy read-only attributes
# `````````````````````````
@lazyattr
def dtype(self):
"""The NumPy ``dtype`` that most closely matches this array."""
return self.atom.dtype
# Properties
# ~~~~~~~~~~
shape = property(
lambda self: (self.nrows,), None, None,
"The shape of the stored array.")
def _get_size_on_disk(self):
raise NotImplementedError('size_on_disk not implemented for VLArrays')
size_on_disk = property(_get_size_on_disk, None, None,
"""
The HDF5 library does not include a function to determine size_on_disk
for variable-length arrays. Accessing this attribute will raise a
NotImplementedError.
""")
size_in_memory = property(
lambda self: self._get_memory_size(), None, None,
"""
The size of this array's data in bytes when it is fully loaded
into memory.
.. note::
When data is stored in a VLArray using the ObjectAtom type,
it is first serialized using pickle, and then converted to
a NumPy array suitable for storage in an HDF5 file.
This attribute will return the size of that NumPy
representation. If you wish to know the size of the Python
objects after they are loaded from disk, you can use this
`ActiveState recipe
<http://code.activestate.com/recipes/577504/>`_.
""")
# Other methods
# ~~~~~~~~~~~~~
def __init__(self, parentnode, name, atom=None, title="",
filters=None, expectedrows=None,
chunkshape=None, byteorder=None,
_log=True):
self._v_version = None
"""The object version of this array."""
self._v_new = new = atom is not None
"""Is this the first time the node has been created?"""
self._v_new_title = title
"""New title for this node."""
self._v_new_filters = filters
"""New filter properties for this array."""
if expectedrows is None:
expectedrows = parentnode._v_file.params['EXPECTED_ROWS_VLARRAY']
self._v_expectedrows = expectedrows
"""The expected number of rows to be stored in the array.
.. versionadded:: 3.0
"""
self._v_chunkshape = None
"""Private storage for the `chunkshape` property of Leaf."""
# Miscellaneous iteration rubbish.
self._start = None
"""Starting row for the current iteration."""
self._stop = None
"""Stopping row for the current iteration."""
self._step = None
"""Step size for the current iteration."""
self._nrowsread = None
"""Number of rows read up to the current state of iteration."""
self._startb = None
"""Starting row for current buffer."""
self._stopb = None
"""Stopping row for current buffer. """
self._row = None
"""Current row in iterators (sentinel)."""
self._init = False
"""Whether we are in the middle of an iteration or not (sentinel)."""
self.listarr = None
"""Current buffer in iterators."""
# Documented (*public*) attributes.
self.atom = atom
"""
An Atom (see :ref:`AtomClassDescr`) instance representing the
*type* and *shape* of the atomic objects to be saved. You may
use a *pseudo-atom* for storing a serialized object or
variable length string per row.
"""
self.nrow = None
"""On iterators, this is the index of the current row."""
self.nrows = None
"""The current number of rows in the array."""
self.extdim = 0 # VLArray only have one dimension currently
"""The index of the enlargeable dimension (always 0 for vlarrays)."""
# Check the chunkshape parameter
if new and chunkshape is not None:
if isinstance(chunkshape, (int, numpy.integer, long)):
chunkshape = (chunkshape,)
try:
chunkshape = tuple(chunkshape)
except TypeError:
raise TypeError(
"`chunkshape` parameter must be an integer or sequence "
"and you passed a %s" % type(chunkshape))
if len(chunkshape) != 1:
raise ValueError("`chunkshape` rank (length) must be 1: %r"
% (chunkshape,))
self._v_chunkshape = tuple(SizeType(s) for s in chunkshape)
super(VLArray, self).__init__(parentnode, name, new, filters,
byteorder, _log)
def _g_post_init_hook(self):
super(VLArray, self)._g_post_init_hook()
self.nrowsinbuf = 100 # maybe enough for most applications
# This is too specific for moving it into Leaf
def _calc_chunkshape(self, expectedrows):
"""Calculate the size for the HDF5 chunk."""
# For computing the chunkshape for HDF5 VL types, we have to
# choose the itemsize of the *each* element of the atom and
# not the size of the entire atom. I don't know why this
# should be like this, perhaps I should report this to the
# HDF5 list.
# F. Alted 2006-11-23
# elemsize = self.atom.atomsize()
elemsize = self._basesize
# AV 2013-05-03
# This is just a quick workaround tha allows to change the API for
# PyTables 3.0 release and remove the expected_mb parameter.
# The algorithm for computing the chunkshape should be rewritten as
# requested by gh-35.
expected_mb = expectedrows * elemsize / 1024. ** 2
chunksize = calc_chunksize(expected_mb)
# Set the chunkshape
chunkshape = chunksize // elemsize
# Safeguard against itemsizes being extremely large
if chunkshape == 0:
chunkshape = 1
return (SizeType(chunkshape),)
def _g_create(self):
"""Create a variable length array (ragged array)."""
atom = self.atom
self._v_version = obversion
# Check for zero dims in atom shape (not allowed in VLArrays)
zerodims = numpy.sum(numpy.array(atom.shape) == 0)
if zerodims > 0:
raise ValueError("When creating VLArrays, none of the dimensions "
"of the Atom instance can be zero.")
if not hasattr(atom, 'size'): # it is a pseudo-atom
self._atomicdtype = atom.base.dtype
self._atomicsize = atom.base.size
self._basesize = atom.base.itemsize
else:
self._atomicdtype = atom.dtype
self._atomicsize = atom.size
self._basesize = atom.itemsize
self._atomictype = atom.type
self._atomicshape = atom.shape
# Compute the optimal chunkshape, if needed
if self._v_chunkshape is None:
self._v_chunkshape = self._calc_chunkshape(self._v_expectedrows)
self.nrows = SizeType(0) # No rows at creation time
# Correct the byteorder if needed
if self.byteorder is None:
self.byteorder = correct_byteorder(atom.type, sys.byteorder)
# After creating the vlarray, ``self._v_objectid`` needs to be
# set because it is needed for setting attributes afterwards.
self._v_objectid = self._create_array(self._v_new_title)
# Add an attribute in case we have a pseudo-atom so that we
# can retrieve the proper class after a re-opening operation.
if not hasattr(atom, 'size'): # it is a pseudo-atom
self.attrs.PSEUDOATOM = atom.kind
return self._v_objectid
def _g_open(self):
"""Get the metadata info for an array in file."""
self._v_objectid, self.nrows, self._v_chunkshape, atom = \
self._open_array()
# Check if the atom can be a PseudoAtom
if "PSEUDOATOM" in self.attrs:
kind = self.attrs.PSEUDOATOM
if kind == 'vlstring':
atom = VLStringAtom()
elif kind == 'vlunicode':
atom = VLUnicodeAtom()
elif kind == 'object':
atom = ObjectAtom()
else:
raise ValueError(
"pseudo-atom name ``%s`` not known." % kind)
elif self._v_file.format_version[:1] == "1":
flavor1x = self.attrs.FLAVOR
if flavor1x == "VLString":
atom = VLStringAtom()
elif flavor1x == "Object":
atom = ObjectAtom()
self.atom = atom
return self._v_objectid
def _getnobjects(self, nparr):
"""Return the number of objects in a NumPy array."""
# Check for zero dimensionality array
zerodims = numpy.sum(numpy.array(nparr.shape) == 0)
if zerodims > 0:
# No objects to be added
return 0
shape = nparr.shape
atom_shape = self.atom.shape
shapelen = len(nparr.shape)
if isinstance(atom_shape, tuple):
atomshapelen = len(self.atom.shape)
else:
atom_shape = (self.atom.shape,)
atomshapelen = 1
diflen = shapelen - atomshapelen
if shape == atom_shape:
nobjects = 1
elif (diflen == 1 and shape[diflen:] == atom_shape):
# Check if the leading dimensions are all ones
# if shape[:diflen-1] == (1,)*(diflen-1):
# nobjects = shape[diflen-1]
# shape = shape[diflen:]
# It's better to accept only inputs with the exact dimensionality
# i.e. a dimensionality only 1 element larger than atom
nobjects = shape[0]
shape = shape[1:]
elif atom_shape == (1,) and shapelen == 1:
# Case where shape = (N,) and shape_atom = 1 or (1,)
nobjects = shape[0]
else:
raise ValueError("The object '%s' is composed of elements with "
"shape '%s', which is not compatible with the "
"atom shape ('%s')." % (nparr, shape, atom_shape))
return nobjects
def get_enum(self):
"""Get the enumerated type associated with this array.
If this array is of an enumerated type, the corresponding Enum instance
(see :ref:`EnumClassDescr`) is returned. If it is not of an enumerated
type, a TypeError is raised.
"""
if self.atom.kind != 'enum':
raise TypeError("array ``%s`` is not of an enumerated type"
% self._v_pathname)
return self.atom.enum
getEnum = previous_api(get_enum)
def append(self, sequence):
"""Add a sequence of data to the end of the dataset.
This method appends the objects in the sequence to a *single row* in
this array. The type and shape of individual objects must be compliant
with the atoms in the array. In the case of serialized objects and
variable length strings, the object or string to append is itself the
sequence.
"""
self._g_check_open()
self._v_file._check_writable()
# Prepare the sequence to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
sequence = atom.toarray(sequence)
statom = atom.base
else:
try: # fastest check in most cases
len(sequence)
except TypeError:
raise TypeError("argument is not a sequence")
statom = atom
if len(sequence) > 0:
# The sequence needs to be copied to make the operation safe
# to in-place conversion.
nparr = convert_to_np_atom2(sequence, statom)
nobjects = self._getnobjects(nparr)
else:
nobjects = 0
nparr = None
self._append(nparr, nobjects)
self.nrows += 1
def iterrows(self, start=None, stop=None, step=None):
"""Iterate over the rows of the array.
This method returns an iterator yielding an object of the current
flavor for each selected row in the array.
If a range is not supplied, *all the rows* in the array are iterated
upon. You can also use the :meth:`VLArray.__iter__` special method for
that purpose. If you only want to iterate over a given *range of rows*
in the array, you may use the start, stop and step parameters.
Examples
--------
::
for row in vlarray.iterrows(step=4):
print('%s[%d]--> %s' % (vlarray.name, vlarray.nrow, row))
.. versionchanged:: 3.0
If the *start* parameter is provided and *stop* is None then the
array is iterated from *start* to the last line.
In PyTables < 3.0 only one element was returned.
"""
(self._start, self._stop, self._step) = self._process_range(
start, stop, step)
self._init_loop()
return self
def __iter__(self):
"""Iterate over the rows of the array.
This is equivalent to calling :meth:`VLArray.iterrows` with default
arguments, i.e. it iterates over *all the rows* in the array.
Examples
--------
::
result = [row for row in vlarray]
Which is equivalent to::
result = [row for row in vlarray.iterrows()]
"""
if not self._init:
# If the iterator is called directly, assign default variables
self._start = 0
self._stop = self.nrows
self._step = 1
# and initialize the loop
self._init_loop()
return self
def _init_loop(self):
"""Initialization for the __iter__ iterator."""
self._nrowsread = self._start
self._startb = self._start
self._row = -1 # Sentinel
self._init = True # Sentinel
self.nrow = SizeType(self._start - self._step) # row number
_initLoop = previous_api(_init_loop)
def next(self):
"""Get the next element of the array during an iteration.
The element is returned as a list of objects of the current
flavor.
"""
if self._nrowsread >= self._stop:
self._init = False
raise StopIteration # end of iteration
else:
# Read a chunk of rows
if self._row + 1 >= self.nrowsinbuf or self._row < 0:
self._stopb = self._startb + self._step * self.nrowsinbuf
self.listarr = self.read(self._startb, self._stopb, self._step)
self._row = -1
self._startb = self._stopb
self._row += 1
self.nrow += self._step
self._nrowsread += self._step
return self.listarr[self._row]
def __getitem__(self, key):
"""Get a row or a range of rows from the array.
If key argument is an integer, the corresponding array row is returned
as an object of the current flavor. If key is a slice, the range of
rows determined by it is returned as a list of objects of the current
flavor.
In addition, NumPy-style point selections are supported. In
particular, if key is a list of row coordinates, the set of rows
determined by it is returned. Furthermore, if key is an array of
boolean values, only the coordinates where key is True are returned.
Note that for the latter to work it is necessary that key list would
contain exactly as many rows as the array has.
Examples
--------
::
a_row = vlarray[4]
a_list = vlarray[4:1000:2]
a_list2 = vlarray[[0,2]] # get list of coords
a_list3 = vlarray[[0,-2]] # negative values accepted
a_list4 = vlarray[numpy.array([True,...,False])] # array of bools
"""
self._g_check_open()
if is_idx(key):
# Index out of range protection
if key >= self.nrows:
raise IndexError("Index out of range")
if key < 0:
# To support negative values
key += self.nrows
(start, stop, step) = self._process_range(key, key + 1, 1)
return self.read(start, stop, step)[0]
elif isinstance(key, slice):
start, stop, step = self._process_range(
key.start, key.stop, key.step)
return self.read(start, stop, step)
# Try with a boolean or point selection
elif type(key) in (list, tuple) or isinstance(key, numpy.ndarray):
coords = self._point_selection(key)
return self._read_coordinates(coords)
else:
raise IndexError("Invalid index or slice: %r" % (key,))
def _assign_values(self, coords, values):
"""Assign the `values` to the positions stated in `coords`."""
for nrow, value in zip(coords, values):
if nrow >= self.nrows:
raise IndexError("First index out of range")
if nrow < 0:
# To support negative values
nrow += self.nrows
object_ = value
# Prepare the object to convert it into a NumPy object
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
object_ = atom.toarray(object_)
statom = atom.base
else:
statom = atom
value = convert_to_np_atom(object_, statom)
nobjects = self._getnobjects(value)
# Get the previous value
nrow = idx2long(
nrow) # To convert any possible numpy scalar value
nparr = self._read_array(nrow, nrow + 1, 1)[0]
nobjects = len(nparr)
if len(value) > nobjects:
raise ValueError("Length of value (%s) is larger than number "
"of elements in row (%s)" % (len(value),
nobjects))
try:
nparr[:] = value
except Exception as exc: # XXX
raise ValueError("Value parameter:\n'%r'\n"
"cannot be converted into an array object "
"compliant vlarray[%s] row: \n'%r'\n"
"The error was: <%s>" % (value, nrow,
nparr[:], exc))
if nparr.size > 0:
self._modify(nrow, nparr, nobjects)
def __setitem__(self, key, value):
"""Set a row, or set of rows, in the array.
It takes different actions depending on the type of the *key*
parameter: if it is an integer, the corresponding table row is
set to *value* (a record or sequence capable of being converted
to the table structure). If *key* is a slice, the row slice
determined by it is set to *value* (a record array or sequence
of rows capable of being converted to the table structure).
In addition, NumPy-style point selections are supported. In
particular, if key is a list of row coordinates, the set of rows
determined by it is set to value. Furthermore, if key is an array of
boolean values, only the coordinates where key is True are set to
values from value. Note that for the latter to work it is necessary
that key list would contain exactly as many rows as the table has.
.. note::
When updating the rows of a VLArray object which uses a
pseudo-atom, there is a problem: you can only update values
with *exactly* the same size in bytes than the original row.
This is very difficult to meet with object pseudo-atoms,
because :mod:`pickle` applied on a Python object does not
guarantee to return the same number of bytes than over another
object, even if they are of the same class.
This effectively limits the kinds of objects than can be
updated in variable-length arrays.
Examples
--------
::
vlarray[0] = vlarray[0] * 2 + 3
vlarray[99] = arange(96) * 2 + 3
# Negative values for the index are supported.
vlarray[-99] = vlarray[5] * 2 + 3
vlarray[1:30:2] = list_of_rows
vlarray[[1,3]] = new_1_and_3_rows
"""
self._g_check_open()
self._v_file._check_writable()
if is_idx(key):
# If key is not a sequence, convert to it
coords = [key]
value = [value]
elif isinstance(key, slice):
(start, stop, step) = self._process_range(
key.start, key.stop, key.step)
coords = range(start, stop, step)
# Try with a boolean or point selection
elif type(key) in (list, tuple) or isinstance(key, numpy.ndarray):
coords = self._point_selection(key)
else:
raise IndexError("Invalid index or slice: %r" % (key,))
# Do the assignment row by row
self._assign_values(coords, value)
# Accessor for the _read_array method in superclass
def read(self, start=None, stop=None, step=1):
"""Get data in the array as a list of objects of the current flavor.
Please note that, as the lengths of the different rows are variable,
the returned value is a *Python list* (not an array of the current
flavor), with as many entries as specified rows in the range
parameters.
The start, stop and step parameters can be used to select only a
*range of rows* in the array. Their meanings are the same as in
the built-in range() Python function, except that negative values
of step are not allowed yet. Moreover, if only start is specified,
then stop will be set to start + 1. If you do not specify neither
start nor stop, then *all the rows* in the array are selected.
"""
self._g_check_open()
start, stop, step = self._process_range_read(start, stop, step)
if start == stop:
listarr = []
else:
listarr = self._read_array(start, stop, step)
atom = self.atom
if not hasattr(atom, 'size'): # it is a pseudo-atom
outlistarr = [atom.fromarray(arr) for arr in listarr]
else:
# Convert the list to the right flavor
flavor = self.flavor
outlistarr = [internal_to_flavor(arr, flavor) for arr in listarr]
return outlistarr
def _read_coordinates(self, coords):
"""Read rows specified in `coords`."""
rows = []
for coord in coords:
rows.append(self.read(coord)[0])
return rows
def _g_copy_with_stats(self, group, name, start, stop, step,
title, filters, chunkshape, _log, **kwargs):
"""Private part of Leaf.copy() for each kind of leaf."""
# Build the new VLArray object
object = VLArray(
group, name, self.atom, title=title, filters=filters,
expectedrows=self._v_expectedrows, chunkshape=chunkshape,
_log=_log)
# Now, fill the new vlarray with values from the old one
# This is not buffered because we cannot forsee the length
# of each record. So, the safest would be a copy row by row.
# In the future, some analysis can be done in order to buffer
# the copy process.
nrowsinbuf = 1
(start, stop, step) = self._process_range_read(start, stop, step)
# Optimized version (no conversions, no type and shape checks, etc...)
nrowscopied = SizeType(0)
nbytes = 0
if not hasattr(self.atom, 'size'): # it is a pseudo-atom
atomsize = self.atom.base.size
else:
atomsize = self.atom.size
for start2 in xrange(start, stop, step * nrowsinbuf):
# Save the records on disk
stop2 = start2 + step * nrowsinbuf
if stop2 > stop:
stop2 = stop
nparr = self._read_array(start=start2, stop=stop2, step=step)[0]
nobjects = nparr.shape[0]
object._append(nparr, nobjects)
nbytes += nobjects * atomsize
nrowscopied += 1
object.nrows = nrowscopied
return (object, nbytes)
_g_copyWithStats = previous_api(_g_copy_with_stats)
def __repr__(self):
"""This provides more metainfo in addition to standard __str__"""
return """%s
atom = %r
byteorder = %r
nrows = %s
flavor = %r""" % (self, self.atom, self.byteorder, self.nrows,
self.flavor)
|